An information processing apparatus, a method for processing an emergency notification, and a program capable of enabling, when an emergency situation for a worker occurs, the worker to accurately recognize a state of issuance of an emergency notification are provided. An information processing apparatus includes a notification transmitting unit configured to transmit an emergency notification when a predetermined condition is satisfied, the emergency notification being a notification for reporting an occurrence of an emergency situation for a worker, and a display control unit configured to perform control so as to display information about the emergency notification on a head-mounted display.

Certain aspects of the present disclosure provide measures, including apparatuses, circuitry, methods and computer programs, for use in monitoring user activity. Orientation data for a user is generated. The generated orientation data is inserted into advertising packets of a wireless communication protocol. The advertising packets comprising the generated orientation data are broadcast according to the wireless communication protocol.

A sensor hub for collecting sensor data during a parachuting or wing-suite event is described herein. In some instances, the sensor hub may include a sensor hub housing, the sensor hub housing including a first cavity that houses a power supply and a communication unit and a second cavity that houses a location sensor and an orientation sensor, wherein the communication unit is configured to share the location sensor and the orientation sensor with a second sensor hub and a helmet that attaches to the sensor hub housing.

In an illustrative embodiment, systems and methods for verifying safety compliance of a worker positioned on a building structure utilize a motion detector module (MDM) for attaching to a wearable safety harness, where the harness has a lifeline attachment feature for connecting the safety harness to a lifeline anchored to the building structure. The MDM may include a motion sensor for detecting movement of the worker and processing circuitry for detecting, from signals provided by the motion sensor, significant movement indicative of the safety harness being properly worn by the worker and/or proper attachment of the safety harness to a lifeline, and translating the significant movements into compliance information. The MDM may include a proximate sensor for detecting contact between the attachment feature on the harness and a fastener on the lifeline for verifying the compliance information.

In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.

A data processing system comprising a processing device configured to collect sets of sensed data representing motion characteristics of a user's gait at a first time and at a second time subsequent to the first time, to process each set of sensed data to form a respective information set dependent on the user's gait at the first and second times, and to compare the first information set with one of (i) the second information set and (ii) a predetermined reference data set in accordance with a predetermined algorithm to identify a condition or change in condition of the user.

A computer-implemented method for detecting medical emergency events may include, via one or more processors, data sensors, and/or transceivers: (1) obtaining sensor data indicative of kinetic actions of a user; (2) analyzing the sensor data to associate the sensor data with a one or more kinetic actions of the user; (3) comparing the one or more kinetic actions of the user with a model of kinetic actions to determine whether the one or more kinetic actions correspond with the model of kinetic actions, with the model being indicative of a medical emergency event; and (4) upon determining that the one or more kinetic actions correspond with the model, contacting medical emergency responders to request medical emergency services for the user. As such, medical emergency events being experienced by a user can be detected, and medical emergency responders may be quickly contacted to provide essential emergency medical services to the individual.

A method and system for training a fall detection classifier using subject-specific movement data. A subject sets a preferred non-fall detection rate. Movement data responsive to a subject's movements during everyday activities are obtained over a predetermined data collection period. For each detected event in the movement data, values for one or more parameters that may (together or individually) indicate a fall are obtained. The obtained values are used to generate a subject-specific non-fall detection rate function. This non-fall detection rate function is used to derive a threshold value, in reference to the subject-set preferred non-fall detection rate, to distinguish fall events from non-fall events.

A user, such as an elderly person, may be assisted by an assistance device in his or her caregiving environment that operates in conjunction with one or more server computers. The assistance device may execute a schedule of assistance actions where each assistance action is associated with a time and is executed at that time to assist the user. An assistance action may present an input request to a user, process a voice input of the user, and analyze the voice input to determine that the voice input corresponds to a negative response event, a positive response event, or a non-response event. Based on the categorization of one or more voice inputs as negative response events, positive response events, or non-response events, it may be determined to notify a caregiver of the user, for example where the user has not responded to a number of assistance actions.

A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.